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Not all people are equally easy to identify: color statistics might be enoughfor some cases while others might require careful reasoning about high- andlow-level details. However, prevailing person re-identification(re-ID) methodsuse one-size-fits-all high-level embeddings from deep convolutional networksfor all cases. This might limit their accuracy on difficult examples or makesthem needlessly expensive for the easy ones. To remedy this, we present a newperson re-ID model that combines effective embeddings built on multipleconvolutional network layers, trained with deep-supervision. On traditionalre-ID benchmarks, our method improves substantially over the previousstate-of-the-art results on all five datasets that we evaluate on. We thenpropose two new formulations of the person re-ID problem underresource-constraints, and show how our model can be used to effectively tradeoff accuracy and computation in the presence of resource constraints. Code andpre-trained models are available at https://github.com/mileyan/DARENet.

Resource Aware Person Re-identification across Multiple Resolutions